sklearn.neighbors.KernelDensity

class sklearn.neighbors.KernelDensity(bandwidth=1.0, algorithm='auto', kernel='gaussian', metric='euclidean', atol=0, rtol=0, breadth_first=True, leaf_size=40, metric_params=None)

Kernel Density Estimation

Read more in the User Guide.

Parameters:

bandwidth : float

The bandwidth of the kernel.

algorithm : string

The tree algorithm to use. Valid options are [‘kd_tree’|’ball_tree’|’auto’]. Default is ‘auto’.

kernel : string

The kernel to use. Valid kernels are [‘gaussian’|’tophat’|’epanechnikov’|’exponential’|’linear’|’cosine’] Default is ‘gaussian’.

metric : string

The distance metric to use. Note that not all metrics are valid with all algorithms. Refer to the documentation of BallTree and KDTree for a description of available algorithms. Note that the normalization of the density output is correct only for the Euclidean distance metric. Default is ‘euclidean’.

atol : float

The desired absolute tolerance of the result. A larger tolerance will generally lead to faster execution. Default is 0.

rtol : float

The desired relative tolerance of the result. A larger tolerance will generally lead to faster execution. Default is 1E-8.

breadth_first : boolean

If true (default), use a breadth-first approach to the problem. Otherwise use a depth-first approach.

leaf_size : int

Specify the leaf size of the underlying tree. See BallTree or KDTree for details. Default is 40.

metric_params : dict

Additional parameters to be passed to the tree for use with the metric. For more information, see the documentation of BallTree or KDTree.

Methods

fit(X[, y])	Fit the Kernel Density model on the data.
get_params([deep])	Get parameters for this estimator.
sample([n_samples, random_state])	Generate random samples from the model.
score(X[, y])	Compute the total log probability under the model.
score_samples(X)	Evaluate the density model on the data.
set_params(**params)	Set the parameters of this estimator.

__init__(bandwidth=1.0, algorithm='auto', kernel='gaussian', metric='euclidean', atol=0, rtol=0, breadth_first=True, leaf_size=40, metric_params=None)

fit(X, y=None)

Fit the Kernel Density model on the data.

Parameters:

X : array_like, shape (n_samples, n_features)

List of n_features-dimensional data points. Each row corresponds to a single data point.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

sample(n_samples=1, random_state=None)

Generate random samples from the model.

Currently, this is implemented only for gaussian and tophat kernels.

Parameters:

n_samples : int, optional

Number of samples to generate. Defaults to 1.

random_state : RandomState or an int seed (0 by default)

A random number generator instance.

Returns:

X : array_like, shape (n_samples, n_features)

List of samples.

score(X, y=None)

Compute the total log probability under the model.

Parameters:

X : array_like, shape (n_samples, n_features)

List of n_features-dimensional data points. Each row corresponds to a single data point.

Returns:

logprob : float

Total log-likelihood of the data in X.

score_samples(X)

Evaluate the density model on the data.

Parameters:

X : array_like, shape (n_samples, n_features)

An array of points to query. Last dimension should match dimension of training data (n_features).

Returns:

density : ndarray, shape (n_samples,)

The array of log(density) evaluations.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self :

Examples using sklearn.neighbors.KernelDensity

Kernel Density Estimation

Kernel Density Estimate of Species Distributions

Simple 1D Kernel Density Estimation